Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study

Kondisi pandemi global Covid-19 yang muncul diakhir tahun 2019 telah menjadi permasalahan utama seluruh negara di dunia. Covid-19 merupakan virus yang menyerang organ paru-paru dan dapat mengakibatkan kematian. Pasien Covid-19 banyak yang telah dirawat di rumah sakit sehingga terdapat data citra chest X-ray paru-paru pasien yang terjangkit Covid-19. Saat ini sudah banyak peneltian yang melakukan klasifikasi citra chest X-ray menggunakan Convolutional Neural Network (CNN) untuk membedakan paru-paru sehat, terinfeksi covid-19, dan penyakit paru-paru lainnya, namun belum ada penelitian yang mencoba membandingkan performa algoritma CNN dan machine learning klasik seperti Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN) untuk mengetahui gap performa dan waktu eksekusi yang dibutuhkan. Penelitian ini bertujuan untuk membandingkan performa dan waktu eksekusi algoritma klasifikasi K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan CNN untuk mendeteksi Covid-19 berdasarkan citra chest X-Ray. Berdasarkan hasil pengujian menggunakan 5 Cross Validation, CNN merupakan algoritma yang memiliki rata-rata performa terbaik yaitu akurasi 0,9591, precision 0,9592, recall 0,9591, dan F1 Score 0,959 dengan waktu eksekusi rata-rata sebesar 3102,562 detik.

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Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

PLoS ONE ◽

10.1371/journal.pone.0242899 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0242899

Author(s):

Musatafa Abbas Abbood Albadr ◽

Sabrina Tiun ◽

Masri Ayob ◽

Fahad Taha AL-Dhief ◽

Khairuddin Omar ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Support Vector Machine ◽

Ct Scan ◽

Extreme Learning Machine ◽

Experimental Result ◽

Support Vector ◽

X Ray ◽

Chest X Ray ◽

Learning Machine

The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

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Classification of Chest X-Ray Images using Wavelet and MFCC Features and Support Vector Machine Classifier

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.4123 ◽

2021 ◽

Vol 11 (4) ◽

pp. 7296-7301

Author(s):

H. A. Owida ◽

A. Al-Ghraibah ◽

M. Altayeb

Keyword(s):

Support Vector Machine ◽

Deep Learning ◽

Support Vector ◽

Svm Classifier ◽

Data Sets ◽

Mel Frequency Cepstral Coefficients ◽

X Ray ◽

Proposed Model ◽

Public Data ◽

Chest X Ray

The shortage and availability limitation of RT-PCR test kits and is a major concern regarding the COVID-19 pandemic. The authorities' intention is to establish steps to control the propagation of the pandemic. However, COVID-19 is radiologically diagnosable using x-ray lung images. Deep learning methods have achieved cutting-edge performance in medical diagnosis software assistance. In this work, a new diagnostic method for detecting COVID-19 disease is implemented using advanced deep learning. Effective features were extracted using wavelet analysis and Mel Frequency Cepstral Coefficients (MFCC) method, and they used in the classification process using the Support Vector Machine (SVM) classifier. A total of 2400 X-ray images, 1200 of them classified as Normal (healthy) and 1200 as COVID-19, have been derived from a combination of public data sets to verify the validity of the proposed model. The experimental results obtained an overall accuracy of 98.8% by using five wavelet features, where the classification using MFCC features, MFCC-delta, and MFCC-delta-delta features reached accuracy around 97% on average. The results show that the proposed model has reached the required level of success to be applicable in COVID 19 diagnosis.

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Support Vector Machine Approach for the Diagnosis of Arthritis From Digital X-Ray Images Using Local Ternary Pattern

Indian Journal Of Applied Research ◽

10.15373/2249555x/sept2013/63 ◽

2011 ◽

Vol 3 (9) ◽

pp. 201-203

Author(s):

Subramoniam.M Subramoniam.M ◽

◽

Rajini.V Rajini.V

Keyword(s):

Support Vector Machine ◽

Support Vector ◽

X Ray ◽

Local Ternary Pattern

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Q-learning System Based on Cooperative Least Squares Support Vector Machine

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2009.00214 ◽

2009 ◽

Vol 35 (2) ◽

pp. 214-219 ◽

Cited By ~ 3

Author(s):

Xue-Song WANG ◽

Xi-Lan TIAN ◽

Yu-Hu CHENG ◽

Jian-Qiang YI

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Learning System ◽

Support Vector ◽

Q Learning

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Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

Mathematical Problems in Engineering ◽

10.1155/2020/8642430 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Shengpu Li ◽

Yize Sun

Keyword(s):

Support Vector Machine ◽

Prediction Model ◽

Least Squares ◽

Transfer Rate ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Experimental Sample ◽

Machine Model ◽

Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

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